用机器学习融合报价与需求查询，大幅提升拍卖效率并减少查询次数。
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
- 结合价值查询与需求查询，更全面地获取竞拍者偏好信息。
- 实验显示效率损失降低至原来的十分之一，查询次数减少58%。
- 适合关注高效拍卖设计与降低竞拍者负担的研究者和实践者。
我们研究迭代组合拍卖（ICAs）的设计问题。核心挑战在于物品组合空间随物品数量呈指数增长。现有基于机器学习（ML）的偏好获取算法通过价值查询仅收集关键信息以提升效率，但实际应用中的拍卖多采用需求查询。本文提出一种新式ML算法，可严格利用价值与需求查询的全部信息，并实验证明二者结合能显著提升学习性能。基于此，我们提出MLHCA——一种新型机器学习驱动的拍卖机制，同时使用价值与需求查询。实验表明，MLHCA显著优于当前最先进方法，效率损失最高降低至1/10，查询次数最多减少58%，在大幅提高效率的同时减轻竞拍者认知负担，为实用性与效率树立新基准。代码已开源：https://github.com/marketdesignresearch/MLHCA。
原文摘要 · Abstract (English)
We study the design of iterative combinatorial auctions (ICAs). The main challenge in this domain is that the bundle space grows exponentially in the number of items. To address this, recent work has proposed machine learning (ML)-based preference elicitation algorithms that aim to elicit only the most critical information from bidders to maximize efficiency. However, while the SOTA ML-based algorithms elicit bidders' preferences via value queries, ICAs that are used in practice elicit information via \emph{demand queries}. In this paper, we introduce a novel ML algorithm that provably makes use of the full information from both value and demand queries, and we show via experiments that combining both query types results in significantly better learning performance in practice. Building on these insights, we present MLHCA, a new ML-powered auction that uses value and demand queries. MLHCA significantly outperforms the previous SOTA, reducing efficiency loss by up to a factor 10, with up to 58\% fewer queries. Thus, MLHCA achieves large efficiency improvements while also reducing bidders' cognitive load, establishing a new benchmark for both practicability and efficiency. Our code is available at https://github.com/marketdesignresearch/MLHCA.
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